Dr. Ning Lu is an Assistant Professor in the Department of Electrical & Computer Engineering at Queen’s University. He is also a Tier 2 Canada Research Chair in Future Communication Networks. Dr. Lu received the B.Eng. (2007) and M.Eng. (2010) degrees from Tongji University, Shanghai, China, and Ph.D. degree (2015) from the University of Waterloo, Waterloo, ON, Canada, all in electrical engineering. Prior to joining Queen’s University, he was an assistant professor in the Department of Computing Science at Thompson Rivers University, Kamloops, BC, Canada. From 2015 to 2016, he was a postdoctoral fellow with the Coordinated Science Laboratory, University of Illinois at Urbana-Champaign. He also spent the summer of 2009 as an intern in the National Institute of Informatics, Tokyo, Japan.
His current research interests include real-time scheduling, distributed algorithms, and reinforcement learning for wireless communication networks. He has published more than 40 papers in top IEEE journals and conferences, including IEEE/ACM Transactions on Networking, IEEE Journal on Selected Areas in Communications, ACM MobiHoc, and IEEE INFOCOM, etc.
Dr. Lu has been the recipient of several awards and fellowships. He and his team won second place in the 2014 Valeo Innovation Challenge for excellence in vehicular networking protocol design. He was presented the best paper award at the 2014 IEEE GLOBECOM conference. He received a NSERC Postdoctoral Fellowship in 2015. Dr. Lu is currently on the editorial board of Encyclopedia of Wireless Networks, Springer. He has served as journal guest editor, TPC member of many IEEE conferences, and reviewer of refereed journals.
Research Interests: Scheduling, computing, learning in communication networks, with applications to Internet of Things, autonomous vehicles, data centers, etc.
For more information about Dr. Lu's research, visit Connected Intelligence Research Lab (CIRL) page. Also, visit his Google Scholar Page.
Recent Publications:
- J. Steiger, B. Li, B. Ji, N. Lu, "Constrained Bandit Learning with Switching Costs for Wireless Networks," In Proc. IEEE International Conference on Computer Communications (INFOCOM), May 2023. [Acceptance rate: 19.2%]
- M. Beitollahi and N. Lu, "FLAC: Federated Learning with Autoencoder Compression and Convergence Guarantee," In Proc. IEEE Global Communications Conference (GLOBECOM), Rio de Janeiro, Brazil, Dec. 2022.
- J. Steiger, B. Li, and N. Lu, "Learning from Delayed Semi-Bandit Feedback under Strong Fairness Guarantees," In Proc. IEEE International Conference on Computer Communications (INFOCOM), May 2022. [Acceptance rate: 19.9%]
- G. Liu, W. Quan, N. Cheng, N. Lu, H. Zhang, and X. Shen, "P4NIS: A P4-based Network Immune System Against Eavesdropping Attacks," Proc. IEEE Infocom'20 Workshop on New IP: The Next Step, Toronto, Canada, July 6-9, 2020.
- X. Kong, N. Lu, B. Li, "Optimal Scheduling for Unmanned Aerial Vehicle Networks with Flow-Level Dynamics," IEEE Transactions on Mobile Computing (Early Access), November 2019.
- N. Lu, Y. Zhou, C. Shi, N. Cheng, L. Cai, and B. Li, "Planning while flying: a measurement-aided dynamic planning of drone small cells," IEEE Internet of Things Journal, Vol. 6, No. 2, pp. 2693-2705, 2018.
- N. Lu, B. Ji, and B. Li, "Age-based Scheduling: Improving Data Freshness for Wireless Real-Time Traffic," Proceedings of ACM MobiHoc 2018, Los Angeles, California, USA, June 2018.
- N. Cheng, W. Xu, W. Shi, Y. Zhou, N. Lu, H. Zhou, and X. Shen, "Air-ground integrated mobile edge networks: architecture, challenges and opportunities," IEEE Communications Magazine, Vol. 56, No. 8, pp. 25-32, 2018.
For a complete list of publications, visit Dr. Lu’s Google Scholar Page.
ELEC 373 Computer Networks, Winter 2020, 2022
ELEC 860 Communication Network Analysis, Winter 2021, 2022
ELEC 326 Probability and Random Processes, Fall 2021